The celebrated Gittins index theorem characterizes the solution to discounted Bayesian MAB problems [1]}, [2]}. Most changes to this model render associated dynamic program (DP) intractable including introducing correlated beliefs, contexts, or a focus on post-experiment performance. A number papers propose heuristic but effective approximations to DP solutions [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}. This paper shares the modeling perspective of those above, but takes a very different algorithmic approach and differs in its emphasis on resilience to confounding.
